There is unequivocal evidence that global sea levels are rising. It is therefore inevitable there will be socioeconomic impacts as a result of this. To aid mitigation, and the implementation of adaptation measures, it is vital the magnitude of the potential impact is quantified. Current approaches in the UK make simplifying assumptions regarding the relationship between present-day and future economic damage relating to coastal flood risk. The analysis undertaken here supports studies of an improved impact of sea level rise by providing national-scale estimates of changes in wave overtopping rates and flood defence overflow rates, as a result of different amounts of sea level rise. The analysis involves the application of components of an existing risk-based coastal flood risk analysis method. A subset of almost 600 flood defence assets around the country has been analysed for sea level rise rates up to 1 m. The resulting analysis shows that, on average, the wave overtopping rate increases by up to 150 times above present-day rates for lower return periods and by up to five times for higher return periods. This differential arises as a result of non-linearities in overtopping rates with increasing extreme sea levels.

With a rise in sea levels, there is a consequent increase in the risk to coastal communities of the severity and magnitude of flood events.However these increased risks are not spatially consistent, and the type and nature of a coastal defence may influence the change in the risk at a location, and to what extent levels of mitigation may need to be implemented.This paper therefore investigates the impact of the change in risk of coastal flooding around England as a result of sea level rise.It involves the application of components of an existing risk-based coastal flood risk analysis method for a subset of almost 600 defences to rises in sea levels up to 1m.The resulting analysis shows that overtopping rates increase at a much greater rate for low return period events, which can be in excess of 100 times.The analysis also shows that the change in overtopping rate is a function of the defence type, with the relative changes for sloping defence structures significantly greater than for vertical defence structures

This paper investigates the inherent inaccuracy in the estimation of various extreme response variables (RVs) for different sea defence structures using joint exceedance curve approaches in common use around the UK. Utilising stochastically generated nearshore datasets that include extreme wave and sea-level conditions determined at regular intervals around the English coastline as part of a previous study, and asset information from the Environment Agency's Asset Information Management System database, this paper assesses 592 sea defence structures and their associated extreme response using different joint exceedance curve approaches when compared against the RV approach. This paper highlights that extreme RVs are often underestimated when using a joint exceedance curve approach, which in many cases can be significant. This suggests that the performance of many sea defence structures are incorrectly estimated. As a consequence, joint exceedance curve approaches may under-design sea defence structures to a greater level than previously indicated, or significantly underestimate extreme RVs when assessing the performance of existing structures.

This paper outlines the evolution of joint probability methods in the design and assessment of sea defence structures in the UK, together with the key drivers for these different methods. It highlights why and how the joint exceedance curve techniques were developed initially from the late 1980s and early 1990s, as well as the reasons for the later development of the Join-Sea software system in the mid-1990s, as well as more recently, the implementation of more robust multivariate statistical approaches. The differences between these techniques are outlined, as well as potential errors accounting for how these different techniques are applied to assess different sea defence response functions.

The Flood and Coastal Engineering (FACE) programme is providing BSc and MSc degrees for Environment Agency staff and others with an interest in flood and coastal erosion risk management. The programme includes an initial 2-year Foundation degree. FACE is sponsored by the Environment Agency and is being implemented by Brunel University and HR Wallingford. The presentation will provide a summary of the main areas of study covered by the degree courses. In addition, some specific learning points covered in the FACE programme will be described to provide an insight into the courses and their importance for flood and coastal erosion risk management. It is intended that the learning points will be of general interest to conference participants and will be based on site visits and practical exercises undertaken by students. The learning points will include problems related to flood water level and flow data and how they can be identified and corrected, a brief summary of how flood probability and flood risk maps are derived and the meaning of the information presented, and roles and responsibilities in flood resilience.

: Abstract The design of breakwaters and other coastal structures around the UK requires the assessment of the joint probability of extreme waves and sea levels. There is a standard simplified approach applied within the UK that uses joint exceedance contours of waves and sea levels. This simplified approach can be non-conservative and lead to the under-design of coastal structures unless correction factors are applied. These limitations have been known for a number of years and alternative, more robust, methods have been applied in the past. These alternative methods are risk-based as they enable the probability of the consequence (structural or serviceability failure) to be established. The more robust methods are not, however, widely used in current practice. This paper describes the development of a statistically robust nearshore multivariate extremes data set around the coastline of England. The dataset can, in principle, be used to undertake risk-based design of structures, thereby overcoming the limitations of existing practice.

It is widely recognised that coastal flood events can arise from combinations of extreme waves and sea levels. For flood risk analysis and the design of coastal structures it is therefore necessary to assess the joint probability of the occurrence of these variables. Traditional methods have involved the application of joint probability contours, defined in terms of extremes of sea conditions that can, if applied without correction factors, lead to the underestimation of flood risk and under-design of coastal structures. This paper describes the application of a robust multivariate statistical model to analyse extreme offshore waves, wind and sea levels around the coast of England. The approach described here is risk based in that it seeks to define extremes of response variables directly, rather than the joint extremes of sea conditions. The output of the statistical model comprises a Monte Carlo simulation of extreme events. These distributions of extreme events have been transformed from offshore to nearshore using a statistical emulator of a wave transformation model. The resulting nearshore extreme sea condition distributions have the potential to be applied for a range of purposes. The application is demonstrated using two structures located on the south coast of England.

It has long been recognised that extreme coastal flooding can arise from the joint occurrence of extreme waves, winds and sea levels. The standard simplified joint probability approach used in England and Wales can result in an underestimation of flood risk unless correction factors are applied. This paper describes the application of a state-of-the-art multivariate extreme value model to offshore winds, waves and sea levels around the coast of England. The methodology overcomes the limitations of the traditional method. The output of the new statistical analysis is a Monte-Carlo (MC) simulation comprising many thousands of offshore extreme events and it is necessary to translate all of these events into overtopping rates for use as input to flood risk assessments. It is computationally impractical to transform all of these MC events from the offshore to the nearshore. Computationally efficient statistical emulators of the SWAN wave transformation model have therefore been constructed. The emulators translate the thousands of MC events offshore. Whilst the methodology has been applied for national flood risk assessment, it has the potential to be implemented for wider use, including climate change impact assessment, nearshore wave climates for detailed local assessments and coastal flood forecasting.

The section of railway which runs along the coastline of south Devon in United Kingdom, from Exeter to Newton Abbot, is one of the most photographed sections of railway in the world. It was opened in 1846 with embankments and seawalls protecting and supporting the railway, providing the route of an atmospheric railway. Despite regular maintenance however, there has been a history of storm damage, one of the most severe occurring in February 2014. This resulted in the collapse of the line, interruption of all rail traffic into and out of the far South- West of the United Kingdom (affecting parts of Devon and the whole of Cornwall) and significant damage to the region’s economy. In order to improve the resilience of the line, several options have been considered to evaluate and reduce climate change impacts to the railway. This paper describes the methodological approach developed to evaluate the risks of flooding for a range of scenarios in the estuary and open coast reaches of the line. Components to derive the present day and future climate change coastal conditions including some possible adaptation measures are also presented together with the results of the hindcasting analysis to assess the performance of the modelling system. An overview of the modelling results obtained to support the development of a long-term Resilience Strategy for asset management is also discussed.

The UK has a long history of coastal flooding, driven by large-scale low-pressure weather systems which can result in flooding over large spatial areas. Traditional coastal flood risk analysis is, however, often undertaken at local scales and hence does not consider the likelihood of simultaneous flooding over larger areas. The flooding within the UK over the Winter of 2013/2014 was notable both for its long duration, lasting over two months, and its spatial extent, affecting many different areas of England and Wales. It is thus apparent that to plan and prepare for these types of extreme event it is necessary to consider the likelihood of flood events arising at different locations simultaneously (i.e. to consider the spatial dependence of extreme flood events). This paper describes the application of a state-of-the-art multivariate extreme value methodology to extreme sea levels and wave conditions around the coast of England and Wales. The output of the analysis comprises a synthetic set of extreme but plausible events that explicitly captures the dependence between sea conditions at different spatial locations around the coast. These simulated extreme events can be used for emergency management and advanced flood risk analysis.

Effective flood risk management requires consideration of a range of different mitigation measures. Depending on the location, these could include structural or nonstructural measures as well as maintenance regimes for existing levee systems. Risk analysis models are used to quantify the benefits, in terms of risk reduction, when introducing different measures; further investigation is required to identify the most appropriate solution to implement. Effective flood risk management decision making requires consideration of a range of performance criteria. Determining the better performing strategies, according to multiple criteria, can be a challenge. This article describes the development of a decision support system that couples a multiobjective optimization algorithm with a flood risk analysis model and an automated cost model. The system has the ability to generate potential mitigation measures that are implemented at different points in time. It then optimizes the performance of the mitigation measures against multiple criteria. The decision support system is applied to an area of the Thames Estuary and the results obtained demonstrate the benefits multiobjective optimization can bring to flood risk management.

Over the past twenty years risk based analysis has become come place in support of flood management decisions. In more recent times it has been increasingly recognised that board-scale analysis of risk is a necessary pre-requisite to effective and efficient policy planning, aiding the prioritisation of medium to long term investment at regional or national scale. The so-called RASP models (Risk assessment for strategic planning) provided a structured analysis of the flood risk system and have been used to estimate the national exposure to flood risk within England and Wales annually since 2002. This paper will explore the quantification of uncertainty in the outputs from such broader scale models taking account of model structure and parameter uncertainties. The approach described uses a combination of structure sensitivity and uncertainty analysis - using hierarchical statistical techniques – practical validation techniques and expert judgement enable important uncertainties to be highlighted. The paper also describes the communication of the uncertainty in the national estimates of flood risk at a national and local level based on the board scale models.

The joint probability of large waves and high water levels is important in estimating coastal flood risk. The greatest risk to coastal defences tends to occur at times of unusually high water levels combined with large waves. Without joint probability analysis (or very long field records) it would be impossible to make reliable estimates of the probability of occurrence of such combined conditions. Defra funded the development and testing of a rigorous joint probability analysis technique called JOIN-SEA. Part of the research programme involved the testing of field and synthetic data sets, each comprising about ten years of records of high tide level, wave height and wave period. Two further data sets were prepared by one of the authors as a blind test to be performed by each of the other authors, without knowledge of the underlying distributions. The intentions were to test each other’s abilities to conduct the analysis, to look at the range of answers that could be produced amongst a group of experienced analysts, and to test the relative importance of the various sources of uncertainty. Results obtained by different analysts are compared, both with each other and with the target results underlying the two synthetic data sets. Results are presented in terms of marginal extremes for wave heights and water levels, joint exceedence extremes, and extreme values of overtopping rate and ‘total water level’.